An easy-to-use real-world multi-objective optimization problem suite

Tanabe, Ryoji; Ishibuchi, Hisao

doi:10.1016/j.asoc.2020.106078

Cited by 194 publications

(85 citation statements)

References 53 publications

(87 reference statements)

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“…In many optimization fields, such as production scheduling, artificial intelligence, combinatorial optimization, large-scale data processing, and data mining, we often encounter many complex optimization problems closer to real life [7][8][9][10][11]. In the real world, the optimization problem is usually multiattribute, which is usually the simultaneous optimization of multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Multimodal Emotion Recognition Model Based on a Deep Neural Network with Multiobjective Optimization

Qiu

Peng

Tang

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of deep learning and wireless communication technology, emotion recognition has received more and more attention from researchers. Computers can only be truly intelligent when they have human emotions, and emotion recognition is its primary consideration. This paper proposes a multimodal emotion recognition model based on a multiobjective optimization algorithm. The model combines voice information and facial information and can optimize the accuracy and uniformity of recognition at the same time. The speech modal is based on an improved deep convolutional neural network (DCNN); the video image modal is based on an improved deep separation convolution network (DSCNN). After single mode recognition, a multiobjective optimization algorithm is used to fuse the two modalities at the decision level. The experimental results show that the proposed model has a large improvement in each evaluation index, and the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The results show that the multiobjective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Multimodal Emotion Recognition Model Based on a Deep Neural Network with Multiobjective Optimization

Qiu

Peng

Tang

et al. 2021

Wireless Communications and Mobile Computing

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“…In the current work, the proposed method is demonstrated on several benchmark problems. It is worth extending our approach to deal with different kinds of MOPs and applications, such as dynamic MOP [45]- [47], feature selection [48]- [51] and real word optimization [52]. These will be directions for future work.…”

Section: Discussionmentioning

confidence: 99%

Combining Global and Local Information for Offspring Generation in Evolutionary Multiobjective Optimization

Long¹,

Liu²,

Jin³

2021

IEEE Access

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The Pareto optimal set of a continuous multiobjective optimization problem shows some kinds of structure, which is called the regularity property and it has been applied to design effective offspring reproduction operators in multiobjective optimization evolutionary algorithms (MOEAs). Usually, the probability model sampling and neighbor based mating are the main strategies to implement the regularity property in designing reproduction operators. In fact, different information is used in these methods. There is no doubt that combining the global and local information will favor the search. To use more information in the offspring reproduction, in this paper, we propose a regularity assisted MOEA, RAMEA for short, that combines Gaussian sampling and neighbor based mating for offspring reproduction. In RAMEA, the k-means clustering method is used to learn the manifold structure information and partition the population into K clusters. A Gaussian probability model is built with K mean vectors of clusters, and K trial offspring solutions are sampled from thus model. Moreover, these sampled trial solutions are added to each cluster as mating parents to generate other offspring solutions. In this way, the global and local information are combined to generate offspring solutions in RAMEA. The proposed approach has been executed in several test instances with complicated characteristics, and compared with seven classical or newly developed MOEAs. The results have demonstrated its advantages over other algorithms.

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“…Experiments are performed using the ZDT [62] problem set for bi-objective optimisation and the DTLZ [25] set for three objective problems. A selection of two and three objective real-world problems from the RE [53] problem set are also used (see Table 1 for details). The chosen RE problems all have continuous variables with known or approximated Pareto Fronts and include: RE2-4-1 (Four bar truss design); RE2-2-4 (Hatch cover design); RE3-5-4 (Vehicle crashworthiness design), and; RE3-4-7 (Rocket injector design).…”

Section: Methodsmentioning

confidence: 99%

Bayesian preference learning for interactive multi-objective optimisation

Taylor

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference

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This work proposes a Bayesian optimisation with Gaussian Process approach to learn decision maker (DM) preferences in the attribute search space of a multi-objective optimisation problem (MOP). The DM is consulted periodically during optimisation of the problem and asked to provide their preference over a series of pairwise comparisons of candidate solutions. After each consultation, the most preferred solution is used as the reference point in an appropriate multiobjective optimisation evolutionary algorithm (MOEA). The rationale for using Bayesian optimisation is to identify the most preferred location in the decision search space with the least number of DM queries, thereby minimising DM cognitive burden and fatigue. This enables non-expert DMs to be involved in the optimisation process and make more informed decisions. We further reduce the number of preference queries required, by progressively redefining the Bayesian search space to reflect the MOEA's decision bounds as it converges toward the Pareto Front. We demonstrate how this approach can locate a reference point close to an unknown preferred location on the Pareto Front, of both benchmark and real-world problems with relatively few pairwise comparisons. CCS CONCEPTS• Applied computing → Multi-criterion optimization and decision-making; • Theory of computation → Interactive computation; Active learning;

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An easy-to-use real-world multi-objective optimization problem suite

Cited by 194 publications

References 53 publications

[Retracted] Multimodal Emotion Recognition Model Based on a Deep Neural Network with Multiobjective Optimization

[Retracted] Multimodal Emotion Recognition Model Based on a Deep Neural Network with Multiobjective Optimization

Combining Global and Local Information for Offspring Generation in Evolutionary Multiobjective Optimization

Bayesian preference learning for interactive multi-objective optimisation

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